class_docstr: This class is intended to represent a set of inter-subject regions
  It should inherit from some abstract multiple ROI class,
  not implemented yet.
docstr: Sample the set of landmark regions
  on the proposed coordinate set cs, assuming a Gaussian shape
  
  Parameters
  ----------
  coord: array of shape(n,dim), optional,
         a set of input coordinates
  pval: float in [0,1]), optional
        cutoff for the CR, i.e.  highest posterior density threshold
  sigma: float, positive, optional
         spatial scale of the spatial model
  
  Returns
  -------
  label: array of shape (n): the posterior labelling
docstr_tok: ["Sample","the","set","of","landmark","regions","on","the","proposed","coordinate","set","cs",",","assuming","a","Gaussian","shape","Parameters","--","--","--","--","--","coord",":","array","of","shape","(","n",",","dim",")",",","optional",",","a","set","of","input","coordinates","pval",":","float","in","[","0,1","]",")",",","optional","cutoff","for","the","CR",",","i.e",".","highest","posterior","density","threshold","sigma",":","float",",","positive",",","optional","spatial","scale","of","the","spatial","model","Returns","--","--","--","-","label",":","array","of","shape","(","n",")",":","the","posterior","labelling"]
code: def map_label(self, coord=None, pval=1.0, sigma=1.0):
      """Sample the set of landmark regions
      on the proposed coordinate set cs, assuming a Gaussian shape
  
      Parameters
      ----------
      coord: array of shape(n,dim), optional,
             a set of input coordinates
      pval: float in [0,1]), optional
            cutoff for the CR, i.e.  highest posterior density threshold
      sigma: float, positive, optional
             spatial scale of the spatial model
  
      Returns
      -------
      label: array of shape (n): the posterior labelling
      """
      if coord is None:
          coord = self.domain.coord
      label = -np.ones(coord.shape[0])
      null_density = 1.0 / self.domain.local_volume.sum()
      if self.k > 0:
          aux = -np.zeros((coord.shape[0], self.k))
          for k in range(self.k):
              kde = self.kernel_density(k, coord, sigma)
              aux[:, k] = _threshold_weight_map(kde, pval)
  
          aux[aux < null_density] = 0
          maux = np.max(aux, 1)
          label[maux > 0] = np.argmax(aux, 1)[maux > 0]
      return label
  
file_path: nipy/nipy/labs/spatial_models/structural_bfls.py
imports: import numpy
  from scipy import stats
label_desc: Use image node_id 4 for calling the LandmarkRegions obj's underlying member method code with example usage: obj.map_label(coord, pval, sigma) and returns: label